Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations103075
Missing cells85
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.4 MiB
Average record size in memory248.0 B

Variable types

Text3
Numeric25
Categorical2
DateTime1

Alerts

Topic_10 is highly overall correlated with Topic_16High correlation
Topic_15 is highly overall correlated with breadthHigh correlation
Topic_16 is highly overall correlated with Topic_10 and 1 other fieldsHigh correlation
Topic_7 is highly overall correlated with Topic_16High correlation
breadth is highly overall correlated with Topic_15 and 1 other fieldsHigh correlation
depth is highly overall correlated with breadthHigh correlation
has_images is highly imbalanced (76.5%) Imbalance
Helpfulness is highly skewed (γ1 = 103.6797507) Skewed
Helpfulness has 93223 (90.4%) zeros Zeros

Reproduction

Analysis started2025-01-22 06:52:45.423081
Analysis finished2025-01-22 06:55:23.379394
Duration2 minutes and 37.96 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:23.740732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length199
Median length166
Mean length134.53837
Min length37

Characters and Unicode

Total characters13867543
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
2nd rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
3rd rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
4th rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
5th rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
ValueCountFrequency (%)
80985
 
3.6%
with 58329
 
2.6%
for 49048
 
2.2%
to 41828
 
1.9%
and 41678
 
1.9%
tv 32241
 
1.4%
wireless 28238
 
1.3%
black 25536
 
1.1%
ipad 22159
 
1.0%
mount 20790
 
0.9%
Other values (692) 1835254
82.1%
2025-01-22T15:55:24.492170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2133011
 
15.4%
e 941651
 
6.8%
o 743740
 
5.4%
a 712480
 
5.1%
t 700457
 
5.1%
i 667637
 
4.8%
r 608608
 
4.4%
n 518027
 
3.7%
l 471464
 
3.4%
s 396834
 
2.9%
Other values (69) 5973634
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13867543
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2133011
 
15.4%
e 941651
 
6.8%
o 743740
 
5.4%
a 712480
 
5.1%
t 700457
 
5.1%
i 667637
 
4.8%
r 608608
 
4.4%
n 518027
 
3.7%
l 471464
 
3.4%
s 396834
 
2.9%
Other values (69) 5973634
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13867543
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2133011
 
15.4%
e 941651
 
6.8%
o 743740
 
5.4%
a 712480
 
5.1%
t 700457
 
5.1%
i 667637
 
4.8%
r 608608
 
4.4%
n 518027
 
3.7%
l 471464
 
3.4%
s 396834
 
2.9%
Other values (69) 5973634
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13867543
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2133011
 
15.4%
e 941651
 
6.8%
o 743740
 
5.4%
a 712480
 
5.1%
t 700457
 
5.1%
i 667637
 
4.8%
r 608608
 
4.4%
n 518027
 
3.7%
l 471464
 
3.4%
s 396834
 
2.9%
Other values (69) 5973634
43.1%

Average_rating
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5064594
Minimum3.8
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:24.700991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile3.9
Q14.4
median4.6
Q34.7
95-th percentile4.8
Maximum4.9
Range1.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.23036215
Coefficient of variation (CV)0.051118214
Kurtosis1.1749486
Mean4.5064594
Median Absolute Deviation (MAD)0.1
Skewness-1.2301552
Sum464503.3
Variance0.053066722
MonotonicityNot monotonic
2025-01-22T15:55:24.898298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4.6 29922
29.0%
4.7 23226
22.5%
4.5 13766
13.4%
4.3 11937
 
11.6%
4.4 6438
 
6.2%
4.8 4824
 
4.7%
3.9 3319
 
3.2%
4.1 2885
 
2.8%
4.2 2686
 
2.6%
3.8 1880
 
1.8%
Other values (2) 2192
 
2.1%
ValueCountFrequency (%)
3.8 1880
 
1.8%
3.9 3319
 
3.2%
4 1108
 
1.1%
4.1 2885
 
2.8%
4.2 2686
 
2.6%
4.3 11937
 
11.6%
4.4 6438
 
6.2%
4.5 13766
13.4%
4.6 29922
29.0%
4.7 23226
22.5%
ValueCountFrequency (%)
4.9 1084
 
1.1%
4.8 4824
 
4.7%
4.7 23226
22.5%
4.6 29922
29.0%
4.5 13766
13.4%
4.4 6438
 
6.2%
4.3 11937
 
11.6%
4.2 2686
 
2.6%
4.1 2885
 
2.8%
4 1108
 
1.1%

Num_of_Rating
Real number (ℝ)

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50408.657
Minimum15398
Maximum223181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:25.127951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum15398
5-th percentile16453
Q121988
median36643
Q362436
95-th percentile122681
Maximum223181
Range207783
Interquartile range (IQR)40448

Descriptive statistics

Standard deviation40912.863
Coefficient of variation (CV)0.81162375
Kurtosis5.0781897
Mean50408.657
Median Absolute Deviation (MAD)16656
Skewness2.1128362
Sum5.1958723 × 109
Variance1.6738624 × 109
MonotonicityNot monotonic
2025-01-22T15:55:25.369738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110444 1977
 
1.9%
33336 1944
 
1.9%
76290 1880
 
1.8%
104579 1770
 
1.7%
24205 1649
 
1.6%
33087 1646
 
1.6%
119789 1630
 
1.6%
18908 1589
 
1.5%
201075 1579
 
1.5%
64529 1578
 
1.5%
Other values (67) 85833
83.3%
ValueCountFrequency (%)
15398 1034
1.0%
15469 1071
1.0%
16023 1181
1.1%
16085 1442
1.4%
16453 1108
1.1%
17206 1436
1.4%
17230 1029
1.0%
17318 1084
1.1%
18061 1005
1.0%
18244 1395
1.4%
ValueCountFrequency (%)
223181 1381
1.3%
201075 1579
1.5%
148591 1144
1.1%
122681 1318
1.3%
119789 1630
1.6%
110468 1342
1.3%
110444 1977
1.9%
104579 1770
1.7%
100244 1549
1.5%
85201 1015
1.0%

Rating
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.4 KiB
5.0
70168 
1.0
12560 
4.0
9568 
3.0
 
5998
2.0
 
4781

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters309225
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row3.0
4th row5.0
5th row1.0

Common Values

ValueCountFrequency (%)
5.0 70168
68.1%
1.0 12560
 
12.2%
4.0 9568
 
9.3%
3.0 5998
 
5.8%
2.0 4781
 
4.6%

Length

2025-01-22T15:55:25.605743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-22T15:55:26.019819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
5.0 70168
68.1%
1.0 12560
 
12.2%
4.0 9568
 
9.3%
3.0 5998
 
5.8%
2.0 4781
 
4.6%

Most occurring characters

ValueCountFrequency (%)
. 103075
33.3%
0 103075
33.3%
5 70168
22.7%
1 12560
 
4.1%
4 9568
 
3.1%
3 5998
 
1.9%
2 4781
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 309225
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 103075
33.3%
0 103075
33.3%
5 70168
22.7%
1 12560
 
4.1%
4 9568
 
3.1%
3 5998
 
1.9%
2 4781
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 309225
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 103075
33.3%
0 103075
33.3%
5 70168
22.7%
1 12560
 
4.1%
4 9568
 
3.1%
3 5998
 
1.9%
2 4781
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 309225
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 103075
33.3%
0 103075
33.3%
5 70168
22.7%
1 12560
 
4.1%
4 9568
 
3.1%
3 5998
 
1.9%
2 4781
 
1.5%
Distinct66230
Distinct (%)64.3%
Missing40
Missing (%)< 0.1%
Memory size805.4 KiB
2025-01-22T15:55:26.443212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length100
Median length87
Mean length21.951415
Min length1

Characters and Unicode

Total characters2261764
Distinct characters300
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60723 ?
Unique (%)58.9%

Sample

1st rowIt's a good product
2nd rownothing came in?
3rd rowGreat for basement or garage use.
4th rowPROS/CONS Good things come in SMALL packages!
5th rowDoesn’t pick up well
ValueCountFrequency (%)
great 18115
 
4.4%
good 10218
 
2.5%
for 9751
 
2.4%
works 9467
 
2.3%
the 9183
 
2.3%
it 7904
 
1.9%
to 7652
 
1.9%
and 6617
 
1.6%
a 6302
 
1.5%
not 6214
 
1.5%
Other values (13646) 316693
77.6%
2025-01-22T15:55:27.157691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
306395
 
13.5%
e 216397
 
9.6%
t 165810
 
7.3%
o 163202
 
7.2%
r 129688
 
5.7%
a 129268
 
5.7%
s 106099
 
4.7%
i 97643
 
4.3%
n 90633
 
4.0%
d 76773
 
3.4%
Other values (290) 779856
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2261764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
306395
 
13.5%
e 216397
 
9.6%
t 165810
 
7.3%
o 163202
 
7.2%
r 129688
 
5.7%
a 129268
 
5.7%
s 106099
 
4.7%
i 97643
 
4.3%
n 90633
 
4.0%
d 76773
 
3.4%
Other values (290) 779856
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2261764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
306395
 
13.5%
e 216397
 
9.6%
t 165810
 
7.3%
o 163202
 
7.2%
r 129688
 
5.7%
a 129268
 
5.7%
s 106099
 
4.7%
i 97643
 
4.3%
n 90633
 
4.0%
d 76773
 
3.4%
Other values (290) 779856
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2261764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
306395
 
13.5%
e 216397
 
9.6%
t 165810
 
7.3%
o 163202
 
7.2%
r 129688
 
5.7%
a 129268
 
5.7%
s 106099
 
4.7%
i 97643
 
4.3%
n 90633
 
4.0%
d 76773
 
3.4%
Other values (290) 779856
34.5%
Distinct95985
Distinct (%)93.2%
Missing45
Missing (%)< 0.1%
Memory size805.4 KiB
2025-01-22T15:55:27.690682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6427
Median length2243
Mean length169.67978
Min length1

Characters and Unicode

Total characters17482108
Distinct characters410
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94777 ?
Unique (%)92.0%

Sample

1st rowThis radio was perfect for my father. He's older (in his 80s) and he wanted a simple transistor radio for the bathroom that runs on batteries. He didn't want anything too fancy or expensive. This fits the bill.
2nd rowI couldn't get any stations in , worthless to me. YouTube videos why I bought it{: buyer beware!
3rd rowThis affordable radio is perfect for my needs. Yet, I miss the quality from the higher end Sony portable. Sorry, the sound is a bit tinny. Yet, I am a fan of the controls, display and design. Good value.
4th rowPROS and CONS and why I chose this one. The story:My mother still lives in independent living with her older sister (she's turning 90, her sister 92).She couldn't get visitors for her birthday because all independent and assisted living places are on lockdown with what's going on.SO I had to find something EASY for her to set up on her own, that was small for her bedside table, and would be a good-sounding radio. EUREKA!PROS:PACKAGING - I was concerned re how it would arrive, but my mother said it was in heavy foam packaging. Came in perfect condition.SIZE - PERFECT SMALL RADIO for her night stand. MUCH smaller than the 22 inch dimensions it shows in top description! It's small enough for a SMALL nightstand.SET UP - MY mother had no problem setting it up or figuring out knobs. This is as easy as radios from the '60's. Basically a couple knobs and a switch to go back and forth between AM and FMSOUND - I was on the phone with my mother as she dialed through various stations and GREAT sound. She was delighted and said, HEAR THIS!PRICE - CAN'T BEAT THE PRICE!
5th rowWaste of money,,,,, can’t get stations on this radio as clear as others
ValueCountFrequency (%)
the 146301
 
4.5%
i 99778
 
3.1%
to 97271
 
3.0%
and 92373
 
2.8%
it 85048
 
2.6%
a 75651
 
2.3%
for 50657
 
1.6%
this 50267
 
1.5%
is 48883
 
1.5%
my 48053
 
1.5%
Other values (58146) 2472124
75.7%
2025-01-22T15:55:28.536044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3207660
18.3%
e 1591444
 
9.1%
t 1315036
 
7.5%
o 1094004
 
6.3%
a 995922
 
5.7%
s 858166
 
4.9%
i 854397
 
4.9%
n 824911
 
4.7%
r 762894
 
4.4%
h 633943
 
3.6%
Other values (400) 5343731
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17482108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3207660
18.3%
e 1591444
 
9.1%
t 1315036
 
7.5%
o 1094004
 
6.3%
a 995922
 
5.7%
s 858166
 
4.9%
i 854397
 
4.9%
n 824911
 
4.7%
r 762894
 
4.4%
h 633943
 
3.6%
Other values (400) 5343731
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17482108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3207660
18.3%
e 1591444
 
9.1%
t 1315036
 
7.5%
o 1094004
 
6.3%
a 995922
 
5.7%
s 858166
 
4.9%
i 854397
 
4.9%
n 824911
 
4.7%
r 762894
 
4.4%
h 633943
 
3.6%
Other values (400) 5343731
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17482108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3207660
18.3%
e 1591444
 
9.1%
t 1315036
 
7.5%
o 1094004
 
6.3%
a 995922
 
5.7%
s 858166
 
4.9%
i 854397
 
4.9%
n 824911
 
4.7%
r 762894
 
4.4%
h 633943
 
3.6%
Other values (400) 5343731
30.6%
Distinct1311
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size805.4 KiB
Minimum2020-01-01 00:00:00
Maximum2023-08-25 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-22T15:55:28.807059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:29.065110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Helpfulness
Real number (ℝ)

Skewed  Zeros 

Distinct66
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21584283
Minimum0
Maximum589
Zeros93223
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:29.317801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum589
Range589
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.0079996
Coefficient of variation (CV)13.936065
Kurtosis16329.198
Mean0.21584283
Median Absolute Deviation (MAD)0
Skewness103.67975
Sum22248
Variance9.0480619
MonotonicityNot monotonic
2025-01-22T15:55:29.576542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93223
90.4%
1 7157
 
6.9%
2 1304
 
1.3%
3 474
 
0.5%
4 263
 
0.3%
5 159
 
0.2%
6 92
 
0.1%
7 81
 
0.1%
8 49
 
< 0.1%
9 37
 
< 0.1%
Other values (56) 236
 
0.2%
ValueCountFrequency (%)
0 93223
90.4%
1 7157
 
6.9%
2 1304
 
1.3%
3 474
 
0.5%
4 263
 
0.3%
5 159
 
0.2%
6 92
 
0.1%
7 81
 
0.1%
8 49
 
< 0.1%
9 37
 
< 0.1%
ValueCountFrequency (%)
589 1
< 0.1%
283 1
< 0.1%
243 1
< 0.1%
235 1
< 0.1%
189 1
< 0.1%
174 1
< 0.1%
136 1
< 0.1%
132 1
< 0.1%
118 1
< 0.1%
117 1
< 0.1%

has_images
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.4 KiB
0
99111 
1
 
3964

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters103075
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 99111
96.2%
1 3964
 
3.8%

Length

2025-01-22T15:55:29.877065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-22T15:55:30.047499image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 99111
96.2%
1 3964
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 99111
96.2%
1 3964
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103075
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 99111
96.2%
1 3964
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103075
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 99111
96.2%
1 3964
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103075
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 99111
96.2%
1 3964
 
3.8%

price
Real number (ℝ)

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.813801
Minimum5.99
Maximum175.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:30.237639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5.99
5-th percentile6.99
Q111.99
median19.99
Q335.99
95-th percentile87.14
Maximum175.99
Range170
Interquartile range (IQR)24

Descriptive statistics

Standard deviation27.729617
Coefficient of variation (CV)0.96237277
Kurtosis9.3561813
Mean28.813801
Median Absolute Deviation (MAD)10
Skewness2.7049529
Sum2969982.5
Variance768.93167
MonotonicityNot monotonic
2025-01-22T15:55:30.492063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.99 8403
 
8.2%
13.99 5825
 
5.7%
29.99 4511
 
4.4%
9.99 4049
 
3.9%
11.99 4016
 
3.9%
15.99 3726
 
3.6%
23.99 2906
 
2.8%
37.99 2844
 
2.8%
39.99 2705
 
2.6%
8.97 2596
 
2.5%
Other values (43) 61494
59.7%
ValueCountFrequency (%)
5.99 1459
1.4%
6.36 1101
1.1%
6.44 1302
1.3%
6.99 2229
2.2%
7.82 1146
1.1%
7.95 1630
1.6%
7.99 1307
1.3%
8.54 1175
1.1%
8.97 2596
2.5%
8.99 2209
2.1%
ValueCountFrequency (%)
175.99 1247
1.2%
118 1442
1.4%
101 1431
1.4%
87.14 1139
1.1%
79.99 1421
1.4%
74.95 1084
1.1%
59.9 1880
1.8%
57 1286
1.2%
55.96 1506
1.5%
51 1408
1.4%

Day_elapsed
Real number (ℝ)

Distinct1333
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean735.80144
Minimum0
Maximum1332
Zeros83
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:30.759320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile121
Q1476
median783
Q31023
95-th percentile1191
Maximum1332
Range1332
Interquartile range (IQR)547

Descriptive statistics

Standard deviation339.54741
Coefficient of variation (CV)0.46146609
Kurtosis-0.91645269
Mean735.80144
Median Absolute Deviation (MAD)265
Skewness-0.37766007
Sum75842733
Variance115292.44
MonotonicityNot monotonic
2025-01-22T15:55:31.019957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1167 179
 
0.2%
1157 174
 
0.2%
1159 174
 
0.2%
1164 173
 
0.2%
1177 171
 
0.2%
1183 166
 
0.2%
1171 164
 
0.2%
1166 164
 
0.2%
1175 161
 
0.2%
1160 159
 
0.2%
Other values (1323) 101390
98.4%
ValueCountFrequency (%)
0 83
0.1%
1 12
 
< 0.1%
2 12
 
< 0.1%
3 12
 
< 0.1%
4 11
 
< 0.1%
5 14
 
< 0.1%
6 24
 
< 0.1%
7 20
 
< 0.1%
8 23
 
< 0.1%
9 23
 
< 0.1%
ValueCountFrequency (%)
1332 3
 
< 0.1%
1331 2
 
< 0.1%
1330 11
< 0.1%
1329 7
< 0.1%
1328 4
 
< 0.1%
1327 8
< 0.1%
1326 12
< 0.1%
1325 13
< 0.1%
1324 12
< 0.1%
1323 10
< 0.1%

depth
Real number (ℝ)

High correlation 

Distinct87509
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5171253
Minimum1.5307932 × 10-17
Maximum1.2552725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:31.483576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.5307932 × 10-17
5-th percentile0.055968751
Q10.35701893
median0.53099879
Q30.67733571
95-th percentile0.85769801
Maximum1.2552725
Range1.2552725
Interquartile range (IQR)0.32031678

Descriptive statistics

Standard deviation0.23769147
Coefficient of variation (CV)0.45964
Kurtosis0.38758293
Mean0.5171253
Median Absolute Deviation (MAD)0.15675475
Skewness-0.00011813119
Sum53302.69
Variance0.056497236
MonotonicityNot monotonic
2025-01-22T15:55:31.751528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.255272505 1465
 
1.4%
0.2758833715 676
 
0.7%
2.624011465 × 10-17662
 
0.6%
2.253146825 × 10-17566
 
0.5%
4.041771262 × 10-17451
 
0.4%
1.821006134 × 10-17414
 
0.4%
0.2538313107 364
 
0.4%
0.2926698596 242
 
0.2%
2.006425501 × 10-17216
 
0.2%
0.3010229154 191
 
0.2%
Other values (87499) 97828
94.9%
ValueCountFrequency (%)
1.530793212 × 10-1744
 
< 0.1%
1.561853735 × 10-17147
 
0.1%
1.596826452 × 10-17160
 
0.2%
1.725082199 × 10-1731
 
< 0.1%
1.741507846 × 10-172
 
< 0.1%
1.821006134 × 10-17414
0.4%
1.83157044 × 10-1728
 
< 0.1%
1.832366319 × 10-172
 
< 0.1%
1.842836764 × 10-171
 
< 0.1%
1.985522588 × 10-17185
0.2%
ValueCountFrequency (%)
1.255272505 1465
1.4%
1.107168528 1
 
< 0.1%
1.099240017 1
 
< 0.1%
1.058747294 1
 
< 0.1%
1.057794226 1
 
< 0.1%
1.055911591 1
 
< 0.1%
1.055776441 1
 
< 0.1%
1.054561942 1
 
< 0.1%
1.053667936 1
 
< 0.1%
1.053293838 1
 
< 0.1%

breadth
Real number (ℝ)

High correlation 

Distinct87241
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2025972
Minimum0.18295671
Maximum5.1752593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:32.016097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.18295671
5-th percentile0.95455813
Q11.5112253
median2.0399289
Q32.8087119
95-th percentile3.975209
Maximum5.1752593
Range4.9923026
Interquartile range (IQR)1.2974866

Descriptive statistics

Standard deviation0.94167132
Coefficient of variation (CV)0.42752769
Kurtosis0.044841791
Mean2.2025972
Median Absolute Deviation (MAD)0.61186324
Skewness0.59757141
Sum227032.71
Variance0.88674488
MonotonicityNot monotonic
2025-01-22T15:55:32.274786image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1829567134 1465
 
1.4%
3.217208462 676
 
0.7%
4.457551225 662
 
0.6%
3.975209001 566
 
0.5%
4.453211933 451
 
0.4%
4.861828035 417
 
0.4%
3.664066636 364
 
0.4%
3.527075707 242
 
0.2%
5.175259307 219
 
0.2%
4.186986283 197
 
0.2%
Other values (87231) 97816
94.9%
ValueCountFrequency (%)
0.1829567134 1465
1.4%
0.3737305658 1
 
< 0.1%
0.392176182 1
 
< 0.1%
0.3987180736 1
 
< 0.1%
0.3989339634 1
 
< 0.1%
0.4091085364 1
 
< 0.1%
0.4181452996 1
 
< 0.1%
0.4287359465 1
 
< 0.1%
0.4383539066 1
 
< 0.1%
0.4394447142 1
 
< 0.1%
ValueCountFrequency (%)
5.175259307 219
0.2%
5.174025577 1
 
< 0.1%
5.169231317 7
 
< 0.1%
5.168322344 1
 
< 0.1%
5.167653468 1
 
< 0.1%
5.157738343 1
 
< 0.1%
5.15599008 1
 
< 0.1%
5.14686582 1
 
< 0.1%
5.133313737 1
 
< 0.1%
5.129788012 1
 
< 0.1%

Topic_1
Real number (ℝ)

Distinct87510
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.045650929
Minimum2.7776449 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:32.539177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.7776449 × 10-20
5-th percentile5.7909239 × 10-20
Q11.1311733 × 10-19
median7.2747183 × 10-18
Q30.0098185833
95-th percentile0.2624424
Maximum1
Range1
Interquartile range (IQR)0.0098185833

Descriptive statistics

Standard deviation0.12082539
Coefficient of variation (CV)2.6467235
Kurtosis25.787249
Mean0.045650929
Median Absolute Deviation (MAD)7.2365382 × 10-18
Skewness4.5049747
Sum4705.4695
Variance0.014598774
MonotonicityNot monotonic
2025-01-22T15:55:32.801840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
0.3315129883 676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1 451
 
0.4%
5.563221153 × 10-20414
 
0.4%
0.2712196809 364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87500) 97828
94.9%
ValueCountFrequency (%)
2.777644924 × 10-201
< 0.1%
2.785353785 × 10-201
< 0.1%
2.848495151 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
2.910738356 × 10-201
< 0.1%
2.928972936 × 10-201
< 0.1%
2.944671804 × 10-201
< 0.1%
ValueCountFrequency (%)
1 451
0.4%
0.9797548364 1
 
< 0.1%
0.9785179766 1
 
< 0.1%
0.9756570272 1
 
< 0.1%
0.9755029433 1
 
< 0.1%
0.9649019448 1
 
< 0.1%
0.9642786396 1
 
< 0.1%
0.9608834349 1
 
< 0.1%
0.9600798121 1
 
< 0.1%
0.9590447542 1
 
< 0.1%

Topic_2
Real number (ℝ)

Distinct87510
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.045513827
Minimum2.7853538 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:33.062345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.7853538 × 10-20
5-th percentile5.7174463 × 10-20
Q11.0677778 × 10-19
median4.2163601 × 10-19
Q30.0061198296
95-th percentile0.30028461
Maximum1
Range1
Interquartile range (IQR)0.0061198296

Descriptive statistics

Standard deviation0.13759017
Coefficient of variation (CV)3.0230412
Kurtosis22.724955
Mean0.045513827
Median Absolute Deviation (MAD)3.7016656 × 10-19
Skewness4.4411219
Sum4691.3378
Variance0.018931056
MonotonicityNot monotonic
2025-01-22T15:55:33.325048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
1 662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
0.4022091922 242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87500) 97828
94.9%
ValueCountFrequency (%)
2.785353785 × 10-201
< 0.1%
2.848824853 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
2.910738356 × 10-201
< 0.1%
2.911897366 × 10-201
< 0.1%
2.935793532 × 10-201
< 0.1%
2.962692799 × 10-201
< 0.1%
2.989243411 × 10-201
< 0.1%
3.027388171 × 10-201
< 0.1%
ValueCountFrequency (%)
1 662
0.6%
0.9913771409 1
 
< 0.1%
0.9902598652 1
 
< 0.1%
0.9881168354 1
 
< 0.1%
0.9858660865 1
 
< 0.1%
0.9837048054 1
 
< 0.1%
0.9824238175 1
 
< 0.1%
0.9823786395 1
 
< 0.1%
0.9810128435 1
 
< 0.1%
0.9807536754 1
 
< 0.1%

Topic_3
Real number (ℝ)

Distinct87509
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.063583267
Minimum2.5908874 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:33.575455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.5908874 × 10-20
5-th percentile5.5632212 × 10-20
Q19.8571277 × 10-20
median2.3058215 × 10-19
Q30.0078623353
95-th percentile0.42987592
Maximum1
Range1
Interquartile range (IQR)0.0078623353

Descriptive statistics

Standard deviation0.16731602
Coefficient of variation (CV)2.6314473
Kurtosis11.62313
Mean0.063583267
Median Absolute Deviation (MAD)1.7145074 × 10-19
Skewness3.3313156
Sum6553.8452
Variance0.027994649
MonotonicityNot monotonic
2025-01-22T15:55:33.888649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
0.6684870117 676
 
0.7%
8.084582409 × 10-20662
 
0.6%
1 566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87499) 97828
94.9%
ValueCountFrequency (%)
2.590887404 × 10-201
< 0.1%
2.777644924 × 10-201
< 0.1%
2.785353785 × 10-201
< 0.1%
2.848495151 × 10-201
< 0.1%
2.848824853 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
2.911897366 × 10-201
< 0.1%
2.944671804 × 10-201
< 0.1%
ValueCountFrequency (%)
1 566
0.5%
0.997680166 1
 
< 0.1%
0.9976382779 1
 
< 0.1%
0.9927431445 1
 
< 0.1%
0.9926009438 1
 
< 0.1%
0.9907045048 1
 
< 0.1%
0.9893300932 1
 
< 0.1%
0.9891242307 1
 
< 0.1%
0.9856401951 1
 
< 0.1%
0.9855167278 1
 
< 0.1%

Topic_4
Real number (ℝ)

Distinct87500
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04223926
Minimum2.5908874 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:34.176799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.5908874 × 10-20
5-th percentile5.5632212 × 10-20
Q11.0853581 × 10-19
median0.0010100955
Q30.021561366
95-th percentile0.25970333
Maximum1
Range1
Interquartile range (IQR)0.021561366

Descriptive statistics

Standard deviation0.10815281
Coefficient of variation (CV)2.5604808
Kurtosis21.445747
Mean0.04223926
Median Absolute Deviation (MAD)0.0010100955
Skewness4.1507546
Sum4353.8117
Variance0.011697031
MonotonicityNot monotonic
2025-01-22T15:55:34.439666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
0.4971449273 191
 
0.2%
Other values (87490) 97828
94.9%
ValueCountFrequency (%)
2.590887404 × 10-201
< 0.1%
2.643524843 × 10-201
< 0.1%
2.777644924 × 10-201
< 0.1%
2.785353785 × 10-201
< 0.1%
2.848824853 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
2.911897366 × 10-201
< 0.1%
ValueCountFrequency (%)
1 47
< 0.1%
0.9998041537 1
 
< 0.1%
0.9994849743 1
 
< 0.1%
0.9993103032 1
 
< 0.1%
0.9990629271 1
 
< 0.1%
0.998892162 1
 
< 0.1%
0.9977134924 1
 
< 0.1%
0.9974907716 1
 
< 0.1%
0.9973337309 1
 
< 0.1%
0.9967029001 1
 
< 0.1%

Topic_5
Real number (ℝ)

Distinct87504
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.054902424
Minimum2.5908874 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:34.700193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.5908874 × 10-20
5-th percentile5.5632212 × 10-20
Q19.5987684 × 10-20
median2.0198465 × 10-19
Q30.013219248
95-th percentile0.39440585
Maximum1
Range1
Interquartile range (IQR)0.013219248

Descriptive statistics

Standard deviation0.15002307
Coefficient of variation (CV)2.73254
Kurtosis14.328288
Mean0.054902424
Median Absolute Deviation (MAD)1.4157159 × 10-19
Skewness3.6364742
Sum5659.0674
Variance0.022506922
MonotonicityNot monotonic
2025-01-22T15:55:35.170910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
1 197
 
0.2%
Other values (87494) 97822
94.9%
ValueCountFrequency (%)
2.590887404 × 10-201
< 0.1%
2.643524843 × 10-201
< 0.1%
2.777644924 × 10-201
< 0.1%
2.785353785 × 10-201
< 0.1%
2.848824853 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
2.928972936 × 10-201
< 0.1%
ValueCountFrequency (%)
1 197
0.2%
0.9999355618 2
 
< 0.1%
0.9997836914 1
 
< 0.1%
0.9996107517 1
 
< 0.1%
0.9996083217 1
 
< 0.1%
0.9995918527 1
 
< 0.1%
0.9995476276 6
 
< 0.1%
0.9995213762 1
 
< 0.1%
0.9994580976 3
 
< 0.1%
0.9993771559 1
 
< 0.1%

Topic_6
Real number (ℝ)

Distinct87509
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04336286
Minimum2.5908874 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:35.427645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.5908874 × 10-20
5-th percentile5.5632212 × 10-20
Q19.7307097 × 10-20
median2.7235331 × 10-19
Q30.0095789429
95-th percentile0.31871965
Maximum1
Range1
Interquartile range (IQR)0.0095789429

Descriptive statistics

Standard deviation0.12787248
Coefficient of variation (CV)2.9488941
Kurtosis16.934881
Mean0.04336286
Median Absolute Deviation (MAD)2.1817864 × 10-19
Skewness3.9457212
Sum4469.6268
Variance0.016351371
MonotonicityNot monotonic
2025-01-22T15:55:35.692388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
0.7287803191 364
 
0.4%
0.5977908078 242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87499) 97828
94.9%
ValueCountFrequency (%)
2.590887404 × 10-201
< 0.1%
2.643524843 × 10-201
< 0.1%
2.848495151 × 10-201
< 0.1%
2.848824853 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
2.910738356 × 10-201
< 0.1%
2.928972936 × 10-201
< 0.1%
2.961824858 × 10-201
< 0.1%
ValueCountFrequency (%)
1 35
< 0.1%
0.9986557843 1
 
< 0.1%
0.9972544841 1
 
< 0.1%
0.9969362335 1
 
< 0.1%
0.9965018011 1
 
< 0.1%
0.9964891522 1
 
< 0.1%
0.9951838185 5
 
< 0.1%
0.9930161138 4
 
< 0.1%
0.9930133896 1
 
< 0.1%
0.9926650978 1
 
< 0.1%

Topic_7
Real number (ℝ)

High correlation 

Distinct87507
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.034390929
Minimum2.5908874 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:35.954364image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.5908874 × 10-20
5-th percentile5.4503483 × 10-20
Q19.8904593 × 10-20
median3.5885418 × 10-19
Q30.0082581208
95-th percentile0.22846411
Maximum1
Range1
Interquartile range (IQR)0.0082581208

Descriptive statistics

Standard deviation0.12578284
Coefficient of variation (CV)3.6574424
Kurtosis29.611709
Mean0.034390929
Median Absolute Deviation (MAD)3.1121744 × 10-19
Skewness5.2073734
Sum3544.845
Variance0.015821324
MonotonicityNot monotonic
2025-01-22T15:55:36.215539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
1 417
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87497) 97825
94.9%
ValueCountFrequency (%)
2.590887404 × 10-201
< 0.1%
2.643524843 × 10-201
< 0.1%
2.777644924 × 10-201
< 0.1%
2.785353785 × 10-201
< 0.1%
2.848495151 × 10-201
< 0.1%
2.848824853 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
ValueCountFrequency (%)
1 417
0.4%
0.9993301398 1
 
< 0.1%
0.9991310986 1
 
< 0.1%
0.9991214517 1
 
< 0.1%
0.9988652285 1
 
< 0.1%
0.9979362869 1
 
< 0.1%
0.9975639447 1
 
< 0.1%
0.996851805 1
 
< 0.1%
0.9966260812 1
 
< 0.1%
0.9963515805 1
 
< 0.1%

Topic_8
Real number (ℝ)

Distinct87491
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.049655189
Minimum2.6435248 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:36.472655image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.6435248 × 10-20
5-th percentile5.5632212 × 10-20
Q11.0213054 × 10-19
median4.7400698 × 10-19
Q30.028613154
95-th percentile0.31458033
Maximum1
Range1
Interquartile range (IQR)0.028613154

Descriptive statistics

Standard deviation0.12001439
Coefficient of variation (CV)2.4169556
Kurtosis15.492745
Mean0.049655189
Median Absolute Deviation (MAD)4.2849742 × 10-19
Skewness3.5942601
Sum5118.2087
Variance0.014403454
MonotonicityNot monotonic
2025-01-22T15:55:36.737123image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87481) 97828
94.9%
ValueCountFrequency (%)
2.643524843 × 10-201
< 0.1%
2.785353785 × 10-201
< 0.1%
2.848495151 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.910738356 × 10-201
< 0.1%
2.911897366 × 10-201
< 0.1%
2.928972936 × 10-201
< 0.1%
2.935793532 × 10-201
< 0.1%
ValueCountFrequency (%)
1 40
< 0.1%
0.9997364551 1
 
< 0.1%
0.9997240389 2
 
< 0.1%
0.9996255001 1
 
< 0.1%
0.9995823325 1
 
< 0.1%
0.9989385805 1
 
< 0.1%
0.9985931064 1
 
< 0.1%
0.9984866948 1
 
< 0.1%
0.9975971367 1
 
< 0.1%
0.9974554692 1
 
< 0.1%

Topic_9
Real number (ℝ)

Distinct87459
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06096481
Minimum2.5908874 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:37.004186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.5908874 × 10-20
5-th percentile5.4228605 × 10-20
Q11.0114428 × 10-19
median0.00013707978
Q30.032440438
95-th percentile0.42000378
Maximum1
Range1
Interquartile range (IQR)0.032440438

Descriptive statistics

Standard deviation0.15113025
Coefficient of variation (CV)2.4789751
Kurtosis12.016398
Mean0.06096481
Median Absolute Deviation (MAD)0.00013707978
Skewness3.3918573
Sum6283.9478
Variance0.022840352
MonotonicityNot monotonic
2025-01-22T15:55:37.279015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87449) 97828
94.9%
ValueCountFrequency (%)
2.590887404 × 10-201
< 0.1%
2.643524843 × 10-201
< 0.1%
2.777644924 × 10-201
< 0.1%
2.785353785 × 10-201
< 0.1%
2.848495151 × 10-201
< 0.1%
2.848824853 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
ValueCountFrequency (%)
1 82
0.1%
0.9996307046 1
 
< 0.1%
0.9995927982 1
 
< 0.1%
0.9994176032 1
 
< 0.1%
0.9993677835 1
 
< 0.1%
0.9993035883 1
 
< 0.1%
0.9988293094 1
 
< 0.1%
0.9987888966 1
 
< 0.1%
0.9985154977 1
 
< 0.1%
0.9984841779 1
 
< 0.1%

Topic_10
Real number (ℝ)

High correlation 

Distinct87507
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.027675259
Minimum2.6435248 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:37.545876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.6435248 × 10-20
5-th percentile5.4228605 × 10-20
Q19.2756565 × 10-20
median2.0407234 × 10-19
Q30.0065564706
95-th percentile0.11675939
Maximum1
Range1
Interquartile range (IQR)0.0065564706

Descriptive statistics

Standard deviation0.1079314
Coefficient of variation (CV)3.8999237
Kurtosis39.308742
Mean0.027675259
Median Absolute Deviation (MAD)1.4749667 × 10-19
Skewness5.9370323
Sum2852.6273
Variance0.011649187
MonotonicityNot monotonic
2025-01-22T15:55:37.814977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
1 219
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87497) 97825
94.9%
ValueCountFrequency (%)
2.643524843 × 10-201
< 0.1%
2.777644924 × 10-201
< 0.1%
2.785353785 × 10-201
< 0.1%
2.848495151 × 10-201
< 0.1%
2.848824853 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
2.910738356 × 10-201
< 0.1%
ValueCountFrequency (%)
1 219
0.2%
0.999922544 1
 
< 0.1%
0.9995767962 7
 
< 0.1%
0.9995151452 1
 
< 0.1%
0.9994400087 1
 
< 0.1%
0.9986418148 1
 
< 0.1%
0.9983825967 1
 
< 0.1%
0.9973888808 1
 
< 0.1%
0.9962358683 1
 
< 0.1%
0.9956357171 1
 
< 0.1%

Topic_11
Real number (ℝ)

Distinct87499
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.049173001
Minimum2.6435248 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:38.073194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.6435248 × 10-20
5-th percentile5.4377258 × 10-20
Q19.7154572 × 10-20
median2.8615133 × 10-19
Q30.016248003
95-th percentile0.32647246
Maximum1
Range1
Interquartile range (IQR)0.016248003

Descriptive statistics

Standard deviation0.13786981
Coefficient of variation (CV)2.8037704
Kurtosis18.496386
Mean0.049173001
Median Absolute Deviation (MAD)2.3390861 × 10-19
Skewness4.0633039
Sum5068.5071
Variance0.019008084
MonotonicityNot monotonic
2025-01-22T15:55:38.331247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87489) 97828
94.9%
ValueCountFrequency (%)
2.643524843 × 10-201
< 0.1%
2.777644924 × 10-201
< 0.1%
2.848495151 × 10-201
< 0.1%
2.848824853 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
2.910738356 × 10-201
< 0.1%
2.911897366 × 10-201
< 0.1%
ValueCountFrequency (%)
1 98
0.1%
0.9993174924 1
 
< 0.1%
0.9989008003 5
 
< 0.1%
0.9987261155 1
 
< 0.1%
0.9986271089 1
 
< 0.1%
0.9981730216 1
 
< 0.1%
0.9981147944 1
 
< 0.1%
0.9980420366 1
 
< 0.1%
0.9977481172 1
 
< 0.1%
0.9977458884 1
 
< 0.1%

Topic_12
Real number (ℝ)

Distinct87507
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.034277814
Minimum2.7776449 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:38.585139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.7776449 × 10-20
5-th percentile5.6119509 × 10-20
Q11.0293542 × 10-19
median4.3930056 × 10-19
Q30.01217713
95-th percentile0.24518977
Maximum1
Range1
Interquartile range (IQR)0.01217713

Descriptive statistics

Standard deviation0.10370933
Coefficient of variation (CV)3.0255526
Kurtosis24.309967
Mean0.034277814
Median Absolute Deviation (MAD)3.9148028 × 10-19
Skewness4.5535248
Sum3533.1856
Variance0.010755625
MonotonicityNot monotonic
2025-01-22T15:55:38.850277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87497) 97828
94.9%
ValueCountFrequency (%)
2.777644924 × 10-201
< 0.1%
2.785353785 × 10-201
< 0.1%
2.848495151 × 10-201
< 0.1%
2.911897366 × 10-201
< 0.1%
2.935793532 × 10-201
< 0.1%
2.944671804 × 10-201
< 0.1%
2.962692799 × 10-201
< 0.1%
3.027388171 × 10-201
< 0.1%
3.0324939 × 10-201
< 0.1%
3.032959316 × 10-201
< 0.1%
ValueCountFrequency (%)
1 32
< 0.1%
0.9998397289 2
 
< 0.1%
0.9994616305 7
 
< 0.1%
0.9985796154 1
 
< 0.1%
0.9977586115 1
 
< 0.1%
0.9967907104 1
 
< 0.1%
0.995778724 1
 
< 0.1%
0.9947031865 2
 
< 0.1%
0.9946687859 1
 
< 0.1%
0.9932475529 1
 
< 0.1%

Topic_13
Real number (ℝ)

Distinct87500
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04779689
Minimum2.5908874 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:39.391305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.5908874 × 10-20
5-th percentile5.5632212 × 10-20
Q11.0492399 × 10-19
median6.3999154 × 10-19
Q30.026547858
95-th percentile0.31200358
Maximum1
Range1
Interquartile range (IQR)0.026547858

Descriptive statistics

Standard deviation0.12734012
Coefficient of variation (CV)2.6641927
Kurtosis19.175658
Mean0.04779689
Median Absolute Deviation (MAD)5.9835665 × 10-19
Skewness4.0876159
Sum4926.6644
Variance0.016215507
MonotonicityNot monotonic
2025-01-22T15:55:39.700675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87490) 97828
94.9%
ValueCountFrequency (%)
2.590887404 × 10-201
< 0.1%
2.848495151 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.910738356 × 10-201
< 0.1%
2.911897366 × 10-201
< 0.1%
2.928972936 × 10-201
< 0.1%
2.935793532 × 10-201
< 0.1%
2.944671804 × 10-201
< 0.1%
2.946672372 × 10-201
< 0.1%
ValueCountFrequency (%)
1 170
0.2%
0.9997963265 1
 
< 0.1%
0.9996145422 1
 
< 0.1%
0.9995487533 1
 
< 0.1%
0.9989347385 1
 
< 0.1%
0.9987921197 1
 
< 0.1%
0.9985725019 1
 
< 0.1%
0.9981870427 1
 
< 0.1%
0.9977732559 1
 
< 0.1%
0.9974521414 1
 
< 0.1%

Topic_14
Real number (ℝ)

Distinct87463
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05836495
Minimum2.5908874 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:39.963538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.5908874 × 10-20
5-th percentile5.4576663 × 10-20
Q11.0236351 × 10-19
median2.5250248 × 10-18
Q30.040741728
95-th percentile0.38103713
Maximum1
Range1
Interquartile range (IQR)0.040741728

Descriptive statistics

Standard deviation0.14277779
Coefficient of variation (CV)2.4462934
Kurtosis14.184795
Mean0.05836495
Median Absolute Deviation (MAD)2.4913208 × 10-18
Skewness3.6309111
Sum6015.9673
Variance0.020385498
MonotonicityNot monotonic
2025-01-22T15:55:40.233232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87453) 97828
94.9%
ValueCountFrequency (%)
2.590887404 × 10-201
< 0.1%
2.643524843 × 10-201
< 0.1%
2.777644924 × 10-201
< 0.1%
2.785353785 × 10-201
< 0.1%
2.848495151 × 10-201
< 0.1%
2.848824853 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
2.910738356 × 10-201
< 0.1%
ValueCountFrequency (%)
1 51
< 0.1%
0.9997698733 1
 
< 0.1%
0.9995476683 1
 
< 0.1%
0.9994311178 1
 
< 0.1%
0.9991681576 1
 
< 0.1%
0.9990427258 1
 
< 0.1%
0.9990026259 1
 
< 0.1%
0.9989360281 1
 
< 0.1%
0.9988634026 1
 
< 0.1%
0.9988568417 1
 
< 0.1%

Topic_15
Real number (ℝ)

High correlation 

Distinct87416
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22153019
Minimum2.5908874 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:40.506340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.5908874 × 10-20
5-th percentile5.5632212 × 10-20
Q10.024578423
median0.14519383
Q30.33632505
95-th percentile0.75157739
Maximum1
Range1
Interquartile range (IQR)0.31174662

Descriptive statistics

Standard deviation0.23954976
Coefficient of variation (CV)1.0813414
Kurtosis0.94769728
Mean0.22153019
Median Absolute Deviation (MAD)0.14258808
Skewness1.2708324
Sum22834.225
Variance0.057384087
MonotonicityNot monotonic
2025-01-22T15:55:40.775162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87406) 97828
94.9%
ValueCountFrequency (%)
2.590887404 × 10-201
< 0.1%
2.643524843 × 10-201
< 0.1%
2.777644924 × 10-201
< 0.1%
2.785353785 × 10-201
< 0.1%
2.848495151 × 10-201
< 0.1%
2.848824853 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
ValueCountFrequency (%)
1 138
0.1%
0.9999921838 1
 
< 0.1%
0.9999408512 1
 
< 0.1%
0.9999231557 1
 
< 0.1%
0.9997653032 1
 
< 0.1%
0.9997510161 1
 
< 0.1%
0.9995837746 1
 
< 0.1%
0.9995384617 1
 
< 0.1%
0.9994418227 1
 
< 0.1%
0.9994174034 1
 
< 0.1%

Topic_16
Real number (ℝ)

High correlation 

Distinct87501
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.038224089
Minimum2.5908874 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:41.036243image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.5908874 × 10-20
5-th percentile5.5500469 × 10-20
Q19.6014572 × 10-20
median2.3548223 × 10-19
Q30.019375842
95-th percentile0.22547374
Maximum1
Range1
Interquartile range (IQR)0.019375842

Descriptive statistics

Standard deviation0.1134377
Coefficient of variation (CV)2.9677019
Kurtosis28.67039
Mean0.038224089
Median Absolute Deviation (MAD)1.7985002 × 10-19
Skewness4.9215039
Sum3939.948
Variance0.012868112
MonotonicityNot monotonic
2025-01-22T15:55:41.301788image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87491) 97828
94.9%
ValueCountFrequency (%)
2.590887404 × 10-201
< 0.1%
2.643524843 × 10-201
< 0.1%
2.777644924 × 10-201
< 0.1%
2.848824853 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
2.911897366 × 10-201
< 0.1%
2.935793532 × 10-201
< 0.1%
2.944671804 × 10-201
< 0.1%
2.961824858 × 10-201
< 0.1%
ValueCountFrequency (%)
1 160
0.2%
0.9996386566 1
 
< 0.1%
0.9996173368 1
 
< 0.1%
0.9991507539 1
 
< 0.1%
0.99882457 2
 
< 0.1%
0.9985286651 1
 
< 0.1%
0.998471555 2
 
< 0.1%
0.9984209261 1
 
< 0.1%
0.9980421919 1
 
< 0.1%
0.9979695104 1
 
< 0.1%

Topic_17
Real number (ℝ)

Distinct87508
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.044257656
Minimum2.7853538 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:41.557210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.7853538 × 10-20
5-th percentile5.6119509 × 10-20
Q19.9833222 × 10-20
median2.6532525 × 10-19
Q30.014927935
95-th percentile0.31981214
Maximum1
Range1
Interquartile range (IQR)0.014927935

Descriptive statistics

Standard deviation0.11955145
Coefficient of variation (CV)2.7012602
Kurtosis16.005329
Mean0.044257656
Median Absolute Deviation (MAD)2.0969304 × 10-19
Skewness3.7453024
Sum4561.8579
Variance0.014292549
MonotonicityNot monotonic
2025-01-22T15:55:41.815502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87498) 97828
94.9%
ValueCountFrequency (%)
2.785353785 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.910738356 × 10-201
< 0.1%
2.928972936 × 10-201
< 0.1%
2.935793532 × 10-201
< 0.1%
2.944671804 × 10-201
< 0.1%
2.946672372 × 10-201
< 0.1%
2.961824858 × 10-201
< 0.1%
2.962692799 × 10-201
< 0.1%
ValueCountFrequency (%)
1 38
< 0.1%
0.9992426853 1
 
< 0.1%
0.9990546219 1
 
< 0.1%
0.9989033884 1
 
< 0.1%
0.9982906917 1
 
< 0.1%
0.9975995456 6
 
< 0.1%
0.9967729009 1
 
< 0.1%
0.9966440369 1
 
< 0.1%
0.9954422417 1
 
< 0.1%
0.9931494276 1
 
< 0.1%

Topic_18
Real number (ℝ)

Distinct87509
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.038436653
Minimum2.5908874 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-22T15:55:42.080676image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.5908874 × 10-20
5-th percentile5.4785428 × 10-20
Q19.6709648 × 10-20
median3.1822209 × 10-19
Q30.015075833
95-th percentile0.25358521
Maximum1
Range1
Interquartile range (IQR)0.015075833

Descriptive statistics

Standard deviation0.12219243
Coefficient of variation (CV)3.1790601
Kurtosis25.727786
Mean0.038436653
Median Absolute Deviation (MAD)2.6929977 × 10-19
Skewness4.7990513
Sum3961.858
Variance0.014930989
MonotonicityNot monotonic
2025-01-22T15:55:42.347252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05555555556 1465
 
1.4%
6.236980013 × 10-20676
 
0.7%
8.084582409 × 10-20662
 
0.6%
6.91741439 × 10-20566
 
0.5%
1.257920032 × 10-19451
 
0.4%
5.563221153 × 10-20414
 
0.4%
5.611950918 × 10-20364
 
0.4%
4.892232006 × 10-20242
 
0.2%
6.14342876 × 10-20216
 
0.2%
5.422860477 × 10-20191
 
0.2%
Other values (87499) 97828
94.9%
ValueCountFrequency (%)
2.590887404 × 10-201
< 0.1%
2.643524843 × 10-201
< 0.1%
2.777644924 × 10-201
< 0.1%
2.785353785 × 10-201
< 0.1%
2.848495151 × 10-201
< 0.1%
2.848824853 × 10-201
< 0.1%
2.870249566 × 10-201
< 0.1%
2.878759536 × 10-201
< 0.1%
2.890360569 × 10-201
< 0.1%
2.891594572 × 10-201
< 0.1%
ValueCountFrequency (%)
1 46
< 0.1%
0.9999011274 1
 
< 0.1%
0.9998999378 1
 
< 0.1%
0.9998979753 76
0.1%
0.9998815585 1
 
< 0.1%
0.9997865879 1
 
< 0.1%
0.9997297063 1
 
< 0.1%
0.9994612581 2
 
< 0.1%
0.999329957 1
 
< 0.1%
0.9991414517 1
 
< 0.1%

Interactions

2025-01-22T15:55:15.783272image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:11.150304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:16.277286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:21.126622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:26.055181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:31.187568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:36.260791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:42.156436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:48.618661image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:53.806289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:58.794383image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:04.062017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:09.263413image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:14.523775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:19.811411image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:24.813681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:29.613653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:34.650408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:39.858404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:44.655572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:49.684645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:54.683313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:59.591344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:05.625408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:10.641576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:15.972455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:11.353441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:16.456145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:21.324491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:26.251170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:31.380197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:36.495754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:42.418534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:48.813337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:53.995747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:58.993070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:04.281605image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:09.451189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:14.712981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:20.005256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:25.000101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:29.802130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:34.847603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:40.050160image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:44.847932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:49.875606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:54.869884image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:59.782657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:05.836311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:11.041055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:16.150887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:11.535634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:16.625791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:21.504719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:26.427820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:31.559944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:36.673519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:42.676206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:48.997969image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:54.172112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:59.193604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:04.520350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:09.629749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:14.889733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:20.180928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:25.193898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:29.980256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:35.039171image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:40.223256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:45.025360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:50.047267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:55.059464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:00.015422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-22T15:55:09.297210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:14.408811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:19.756370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:15.110095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:19.755629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:24.852833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:29.942320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:35.064112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:40.421307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:47.460421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:52.620151image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:57.609662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:02.813102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:08.112243image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:13.313066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:18.660389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:23.682913image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:28.508655image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:33.520572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:38.503360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:43.525570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:48.543466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:53.342749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:58.395930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:04.118959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:09.484506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:14.607018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:19.947957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:15.320193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:19.941551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:25.040100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:30.131290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:35.259876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:40.716458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:47.657790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:52.825234image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:57.796837image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:03.000621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:08.298574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:13.548687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:18.861277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:23.874535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:28.694523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:33.709432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:38.687990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:43.723883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:48.732113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:53.531425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:58.601997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:04.347370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:09.672054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:14.799844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:20.173221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:15.512599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:20.126257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:25.220760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:30.324647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:35.457261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:41.016275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:47.854851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:53.011303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:57.985450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:03.185874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:08.490237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:13.769238image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:19.049742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:24.060527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:28.878123image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:33.895566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:39.107253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:43.907782image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:48.932212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:53.720977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:58.788626image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:04.627761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:09.868782image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:15.008515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:20.415552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:15.704260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:20.574101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:25.416152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:30.522397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:35.657940image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:41.352502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:48.048026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:53.231960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:58.187072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:03.391955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:08.685110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:13.958674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:19.245566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:24.247469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:29.056862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:34.084721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:39.296044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:44.095090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:49.115977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:53.911189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:58.970986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:04.909983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:10.063274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:15.202845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:20.615138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:15.897181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:20.760515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:25.634596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:30.720538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:35.842793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:41.621145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:48.232582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:53.427049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:58.376487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:03.671335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:08.878302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:14.153575image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:19.430891image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:24.434293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:29.241373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:34.269673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:39.479239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:44.279173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:49.306396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:54.092319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:59.183357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:05.176785image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:10.251253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:15.395162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:20.811503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:16.086471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:20.944781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:25.864765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:30.944283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:36.035511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:41.897006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:48.425699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:53.624775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:53:58.606451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:03.879358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:09.068806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:14.334730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:19.624198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:24.625594image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:29.427443image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:34.457443image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:39.672299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:44.471079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:49.491682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:54.494198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:54:59.395560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:05.417535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:10.446941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-22T15:55:15.587559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-22T15:55:42.809482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Average_ratingDay_elapsedHelpfulnessNum_of_RatingRatingTopic_1Topic_10Topic_11Topic_12Topic_13Topic_14Topic_15Topic_16Topic_17Topic_18Topic_2Topic_3Topic_4Topic_5Topic_6Topic_7Topic_8Topic_9breadthdepthhas_imagesprice
Average_rating1.0000.079-0.0290.2160.1230.0290.057-0.1520.004-0.028-0.002-0.0960.034-0.006-0.030-0.002-0.007-0.078-0.0330.0030.026-0.1170.0450.067-0.0200.080-0.013
Day_elapsed0.0791.000-0.086-0.1480.0370.025-0.014-0.062-0.009-0.021-0.011-0.061-0.006-0.017-0.039-0.020-0.019-0.030-0.0430.0030.018-0.034-0.0260.0390.0020.035-0.014
Helpfulness-0.029-0.0861.000-0.0220.003-0.024-0.0120.1120.0220.0750.0580.1240.0150.0460.0410.030-0.0270.065-0.0200.0360.0100.0650.055-0.1320.0900.0300.053
Num_of_Rating0.216-0.148-0.0221.0000.052-0.0140.0130.0100.005-0.078-0.0960.032-0.0530.0270.023-0.0140.066-0.055-0.0140.015-0.071-0.117-0.0280.038-0.0940.031-0.060
Rating0.1230.0370.0030.0521.0000.0910.0540.1430.0470.0410.0410.1440.0400.0270.0410.0550.0730.0250.0650.0260.0620.0350.0220.0870.0710.0190.074
Topic_10.0290.025-0.024-0.0140.0911.0000.2220.0540.2020.1200.0850.1290.2220.1100.1200.1620.1470.1120.1330.1500.2190.1470.108-0.2520.2460.0280.055
Topic_100.057-0.014-0.0120.0130.0540.2221.0000.2110.2610.3390.2560.3210.5160.2180.4260.2100.2200.3050.2690.2480.4630.2310.334-0.3620.2880.0190.009
Topic_11-0.152-0.0620.1120.0100.1430.0540.2111.0000.1920.3000.3130.3600.2290.1950.2590.1530.1370.2750.1350.2250.2260.2340.269-0.3740.2310.0270.045
Topic_120.004-0.0090.0220.0050.0470.2020.2610.1921.0000.2540.2470.2320.3270.4320.2390.3170.0920.1520.2770.3350.3080.3500.214-0.3600.3550.0260.114
Topic_13-0.028-0.0210.075-0.0780.0410.1200.3390.3000.2541.0000.3290.2740.4930.2020.3180.2780.1290.3000.1620.1750.3970.2860.252-0.4450.4300.0310.048
Topic_14-0.002-0.0110.058-0.0960.0410.0850.2560.3130.2470.3291.0000.3120.3050.2000.2120.1930.1560.3580.1730.2380.3260.2680.242-0.3910.2730.0100.049
Topic_15-0.096-0.0610.1240.0320.1440.1290.3210.3600.2320.2740.3121.0000.2710.2480.3390.1830.2340.2700.2160.2760.2620.2380.291-0.7070.1940.0460.031
Topic_160.034-0.0060.015-0.0530.0400.2220.5160.2290.3270.4930.3050.2711.0000.2410.3920.3380.1530.3160.2930.2230.5120.3000.308-0.4140.3820.0290.084
Topic_17-0.006-0.0170.0460.0270.0270.1100.2180.1950.4320.2020.2000.2480.2411.0000.2760.2580.1040.1340.1750.2900.2120.4210.224-0.3670.3280.0190.015
Topic_18-0.030-0.0390.0410.0230.0410.1200.4260.2590.2390.3180.2120.3390.3920.2761.0000.2050.2530.2890.2300.2410.2960.2300.359-0.3950.3070.020-0.007
Topic_2-0.002-0.0200.030-0.0140.0550.1620.2100.1530.3170.2780.1930.1830.3380.2580.2051.0000.0440.1380.1590.1590.2040.2620.133-0.3080.2900.0290.065
Topic_3-0.007-0.019-0.0270.0660.0730.1470.2200.1370.0920.1290.1560.2340.1530.1040.2530.0441.0000.1300.1370.1400.1110.1010.224-0.2980.1490.038-0.108
Topic_4-0.078-0.0300.065-0.0550.0250.1120.3050.2750.1520.3000.3580.2700.3160.1340.2890.1380.1301.0000.2190.1290.3520.2620.195-0.4090.3820.0190.049
Topic_5-0.033-0.043-0.020-0.0140.0650.1330.2690.1350.2770.1620.1730.2160.2930.1750.2300.1590.1370.2191.0000.2680.2800.1800.323-0.3030.2010.0350.169
Topic_60.0030.0030.0360.0150.0260.1500.2480.2250.3350.1750.2380.2760.2230.2900.2410.1590.1400.1290.2681.0000.2500.1860.254-0.3300.2400.0210.053
Topic_70.0260.0180.010-0.0710.0620.2190.4630.2260.3080.3970.3260.2620.5120.2120.2960.2040.1110.3520.2800.2501.0000.2900.249-0.3800.3590.0300.154
Topic_8-0.117-0.0340.065-0.1170.0350.1470.2310.2340.3500.2860.2680.2380.3000.4210.2300.2620.1010.2620.1800.1860.2901.0000.230-0.3890.3700.0260.127
Topic_90.045-0.0260.055-0.0280.0220.1080.3340.2690.2140.2520.2420.2910.3080.2240.3590.1330.2240.1950.3230.2540.2490.2301.000-0.3910.2820.047-0.009
breadth0.0670.039-0.1320.0380.087-0.252-0.362-0.374-0.360-0.445-0.391-0.707-0.414-0.367-0.395-0.308-0.298-0.409-0.303-0.330-0.380-0.389-0.3911.000-0.7550.071-0.063
depth-0.0200.0020.090-0.0940.0710.2460.2880.2310.3550.4300.2730.1940.3820.3280.3070.2900.1490.3820.2010.2400.3590.3700.282-0.7551.0000.0820.075
has_images0.0800.0350.0300.0310.0190.0280.0190.0270.0260.0310.0100.0460.0290.0190.0200.0290.0380.0190.0350.0210.0300.0260.0470.0710.0821.0000.077
price-0.013-0.0140.053-0.0600.0740.0550.0090.0450.1140.0480.0490.0310.0840.015-0.0070.065-0.1080.0490.1690.0530.1540.127-0.009-0.0630.0750.0771.000

Missing values

2025-01-22T15:55:21.194549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-22T15:55:22.197058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-22T15:55:22.931974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

product_nameAverage_ratingNum_of_RatingRatingreview_titleReview_TextPosted_DateHelpfulnesshas_imagespriceDay_elapseddepthbreadthTopic_1Topic_2Topic_3Topic_4Topic_5Topic_6Topic_7Topic_8Topic_9Topic_10Topic_11Topic_12Topic_13Topic_14Topic_15Topic_16Topic_17Topic_18
0Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276185.0It's a good productThis radio was perfect for my father. He's older (in his 80s) and he wanted a simple transistor radio for the bathroom that runs on batteries. He didn't want anything too fancy or expensive. This fits the bill.2022-04-210034.953480.6663671.9571703.315746e-038.255930e-031.223670e-193.312529e-021.223670e-191.223670e-191.386962e-030.0364451.518639e-024.804550e-016.473143e-021.223670e-195.337728e-033.930807e-030.2773494.128901e-021.223670e-192.919161e-02
1Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276181.0nothing came in?I couldn't get any stations in , worthless to me. YouTube videos why I bought it{: buyer beware!2022-08-290034.952180.4108401.3592285.420243e-195.420243e-195.420243e-195.388550e-025.420243e-195.420243e-193.181625e-020.0407424.585685e-025.420243e-197.459509e-025.420243e-195.420243e-195.420243e-190.7531055.420243e-195.420243e-195.420243e-19
2Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276183.0Great for basement or garage use.This affordable radio is perfect for my needs. Yet, I miss the quality from the higher end Sony portable. Sorry, the sound is a bit tinny. Yet, I am a fan of the controls, display and design. Good value.2021-07-161034.956270.7411532.1215957.971423e-031.605523e-018.585351e-204.246127e-038.585351e-208.585351e-201.128432e-030.2143311.100210e-032.767580e-018.585351e-201.198606e-024.023152e-034.721610e-030.0284229.360382e-032.632406e-011.215887e-02
3Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276185.0PROS/CONS Good things come in SMALL packages!PROS and CONS and why I chose this one. The story:My mother still lives in independent living with her older sister (she's turning 90, her sister 92).She couldn't get visitors for her birthday because all independent and assisted living places are on lockdown with what's going on.SO I had to find something EASY for her to set up on her own, that was small for her bedside table, and would be a good-sounding radio. EUREKA!PROS:PACKAGING - I was concerned re how it would arrive, but my mother said it was in heavy foam packaging. Came in perfect condition.SIZE - PERFECT SMALL RADIO for her night stand. MUCH smaller than the 22 inch dimensions it shows in top description! It's small enough for a SMALL nightstand.SET UP - MY mother had no problem setting it up or figuring out knobs. This is as easy as radios from the '60's. Basically a couple knobs and a switch to go back and forth between AM and FMSOUND - I was on the phone with my mother as she dialed through various stations and GREAT sound. She was delighted and said, HEAR THIS!PRICE - CAN'T BEAT THE PRICE!2020-04-080034.9510910.9077081.3795743.249549e-025.870766e-027.089108e-201.839122e-021.758229e-017.089108e-202.173170e-030.1128631.699631e-022.254361e-017.089108e-202.031302e-011.123276e-027.205011e-030.1072981.795924e-027.089108e-201.028973e-02
4Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276181.0Doesn’t pick up wellWaste of money,,,,, can’t get stations on this radio as clear as others2020-12-250034.958300.5664271.2767948.978169e-034.023997e-023.508924e-193.508924e-191.661436e-023.508924e-193.508924e-190.2393433.508924e-193.508924e-198.484912e-023.508924e-193.508924e-193.508924e-190.5556594.261227e-023.508924e-191.170387e-02
5Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276185.0Surprise!Surprisingly wonderful little radio. Just what we wanted!!2020-04-100034.9510890.2422131.6776015.044189e-036.656610e-032.005952e-191.128813e-032.005952e-192.005952e-191.232543e-030.0591292.005952e-192.005952e-192.005952e-192.005952e-192.005952e-192.005952e-190.8685844.829030e-022.005952e-199.934057e-03
6Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276184.0Fair to good receptionVery good portable radio. Great size. Fair to good reception in a difficult reception area.I have tried more expensive radios that didpoorly.2020-05-230034.9510460.6579702.0304301.522906e-015.527536e-011.481011e-191.374851e-021.768518e-031.481011e-194.351401e-030.0568951.481011e-193.300156e-021.214588e-021.481011e-194.563454e-031.374432e-020.1153233.293433e-021.481011e-196.479946e-03
7Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276184.0Cute LITTLE thing. For local stations only.Works great for background music and local news in home office.Pros:-AC (electric cord) or 4 AA batteries.-VERY portable—it’s small. See photos.-Very simple to use.-Easy to read numbers.-Throwback classic radio look.-Sound from the one speaker is clear — if the station comes in.Cons:-Pulls in local stations (very local) but not half as many as your car radio (those are built to a very robust standard). If your stations are 25+ miles away, you probably won’t get them unless they have a powerful transmitter or you’re in a very good spot for reception.-Just basic mono sound but you shouldn’t be buying this for sound quality anyway.-You might expect something bigger for $292021-03-130134.957520.8423681.1821174.008836e-027.402859e-027.725443e-029.411598e-027.704182e-028.336627e-202.552300e-030.3322748.336627e-201.444663e-038.336627e-208.336627e-208.336627e-208.336627e-200.1737206.259150e-031.171463e-014.073802e-03
8Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276185.0good purchase!I really like this radio!2021-12-110034.954790.2876593.3553091.683860e-031.342919e-028.030011e-044.476180e-038.428160e-047.855019e-207.855019e-200.0444957.855019e-207.855019e-207.855019e-205.371250e-038.506146e-017.855019e-200.0405443.774014e-027.855019e-207.855019e-20
9Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276185.0Exactly as picturedDoesn't use much power, perfect for camping, brings in lots of stations.2021-05-040034.957000.4819053.2319731.011051e-191.011051e-191.011051e-193.165537e-011.011051e-191.011051e-191.011051e-190.0007592.418039e-025.473736e-018.883712e-021.011051e-194.967658e-031.011051e-190.0139891.011051e-191.011051e-193.338953e-03
product_nameAverage_ratingNum_of_RatingRatingreview_titleReview_TextPosted_DateHelpfulnesshas_imagespriceDay_elapseddepthbreadthTopic_1Topic_2Topic_3Topic_4Topic_5Topic_6Topic_7Topic_8Topic_9Topic_10Topic_11Topic_12Topic_13Topic_14Topic_15Topic_16Topic_17Topic_18
103065Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0awesome covering ,love that i can prop it up for versatility of use .nothing2020-12-310019.998231.2552730.1829575.555556e-025.555556e-025.555556e-025.555556e-025.555556e-025.555556e-025.555556e-025.555556e-025.555556e-025.555556e-025.555556e-025.555556e-025.555556e-025.555556e-025.555556e-025.555556e-025.555556e-025.555556e-02
103066Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0Love itiPad air2022-08-110019.992350.7169952.0820433.013937e-032.488566e-194.532236e-045.440426e-022.488566e-192.488566e-198.302040e-022.488566e-192.488566e-191.468969e-012.488566e-192.488566e-191.228121e-012.488566e-191.095785e-014.425508e-012.488566e-193.726997e-02
103067Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0BeautifulI am pleased with this iPad cover. It is bulkier than I wanted, but I am overall pleased.2020-11-030019.998810.8829031.1696347.760633e-045.740484e-032.615706e-193.270914e-022.615706e-194.223599e-026.039255e-023.163008e-024.298069e-039.126474e-022.615706e-191.880647e-021.046104e-012.615706e-192.341361e-013.095711e-012.379145e-024.003737e-02
103068Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0Love!Love this cover. Great quality love the pocket too!2023-03-240019.99100.4146993.3415219.011482e-025.530904e-205.530904e-205.530904e-205.530904e-205.530904e-206.127162e-015.530904e-205.530904e-205.530904e-205.530904e-205.530904e-204.613110e-035.530904e-205.530904e-201.309275e-022.794631e-015.530904e-20
103069Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434304.0I love it!Cover for my iPad. Love the color2021-08-310019.995800.4661663.2193201.112639e-031.270385e-039.099896e-201.146736e-029.099896e-209.099896e-206.722265e-019.099896e-209.099896e-204.573007e-029.099896e-209.099896e-206.080102e-029.099896e-201.708907e-021.791282e-019.099896e-201.117478e-02
103070Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0Great iPad cover at an economical price!It’s very sturdy & protective! Easy to position for free standing.2022-01-230019.994350.4634192.7417591.444123e-191.718175e-031.444123e-192.404001e-027.529695e-013.890807e-031.532096e-021.444123e-192.344425e-022.121436e-021.444123e-199.833378e-031.836920e-021.002671e-026.259880e-039.361722e-026.451316e-041.865045e-02
103071Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0Best iPad caseNothing to dislike. Excellent quality, easy to use and good price.2022-06-170019.992900.7071902.1999733.324558e-201.400008e-013.324558e-201.529408e-011.560661e-011.773038e-023.324558e-203.324558e-203.324558e-203.324558e-203.324558e-202.567095e-013.324558e-203.324558e-203.324558e-203.324558e-202.765524e-013.324558e-20
103072Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0Durable cover for iPad!Love this product. Color is pretty.Easy to hold and prop up.Uses: reading books, playing games, Searching internet.2021-03-010019.997630.6669482.2499018.989080e-205.671156e-038.989080e-201.544330e-022.465011e-012.740441e-013.564762e-012.348816e-038.989080e-203.619380e-038.989080e-208.989080e-201.270322e-028.989080e-204.442316e-023.060205e-028.989080e-208.167454e-03
103073Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0Worth itQuick shipping. Fits ipad easily. Nice colors.2021-01-190019.998040.3894573.3369303.737395e-031.247881e-031.103581e-191.242429e-028.681694e-037.092952e-032.484914e-021.103581e-199.434774e-044.381659e-021.103581e-198.304560e-032.470403e-029.565128e-033.163128e-028.138633e-013.026517e-036.111749e-03
103074Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0Great!Sturdy, easy to hold and maneuver, durable2020-06-250019.9910120.5674702.3874991.436895e-033.853880e-031.129984e-192.315500e-026.601817e-017.700153e-042.786014e-021.129984e-198.794907e-031.949897e-021.129984e-191.346313e-022.713332e-021.129984e-193.347718e-021.452410e-011.340503e-022.172887e-02